Learn Machine Learning Classification Algorithms using MATLAB and learn how to implement to use different machine learning algorithms using MATLAB.
Machine learning classification algorithms is one of the most complicated topics to understand but this course is especially designed in a stepwise manner which will cover the basics of MATLAB and will also educate learners on how to use different machine learning algorithms using MATLAB.
This course will be covering classification algorithms such as K-Nearest Neighbor, Naive Bayes, Discriminant Analysis, Decision Tress, Support Vector Machines, Error-Correcting Output Codes and Ensembles. Also, you’ll get to learn how to cross validate these models & evaluate their performances.
Layout of the complete course will be:
Segment 1: Instructor and Course Introduction
Segment 2: MATLAB Crash Course
Segment 3: Grabbing and Importing Dataset
Segment 4: K-Nearest Neighbor
Segment 5: Naive Bayes
Segment 6: Decision Trees
Segment 7: Discriminant Analysis
Segment 8: Support Vector Machines
Segment 9: Error Correcting Output Codes
Segment 10: Classification with Ensembles
Segment 11: Validation Methods
Segment 12: Evaluating Performance
Outcomes on completion of this course:
- In-depth knowledge on implementation of machine learning algorithms using MATLAB
- Effective performing of analysis on the data
Some exceptional benefits associated with this course enrollment are:
- Quality course material
- Lifetime access to the course
- Instant & free course updates
- Access to all Questions & Answers initiated by other students as well
- Personalized support from the instructor’s end on any issue related to the course
- Few free lectures for a quick overview
- Be loud and clear amongst the crowd
- Adapt personality that creates a connection
- Make self-image of audiences at a personal level
It’s time for you to grab the opportunity and make the most out of your life.
Enroll today!!
This course will be providing various practice options in between the lessons, which you’ll find highly useful for better learning. Just a few minutes a day can transform your career growth.
Don’t miss this unique opportunity
Curriculum
- 12 Sections
- 56 Lessons
- 52 Weeks
- Instructor and Course Introduction4
- MATLAB Crash Course3
- Grabbing and Importing a Dataset4
- K-Nearest Neighbor7
- 4.1Nearest Neighbor Intuition9 Minutes
- 4.2Nearest Neighbor in MATLAB10 Minutes
- 4.3Learning KNN model with features subset and with non-numeric data11 Minutes
- 4.4Dealing with scalling issue and copying a learned model (4)3 Minutes
- 4.5Types of Properties (5)11 Minutes
- 4.6Building a model with subset of classes, missing values and instances weights (6)7 Minutes
- 4.7Properties of KNN5 Minutes
- Naive Bayes5
- Decision Trees6
- Discriminant Analysis4
- Support Vector Machines4
- Error Correcting Output Codes5
- Classification with Ensembles2
- Validation Methods2
- Performance Evaluation10
- 12.1Making Predictions with the Models8 Minutes
- 12.2Determining the classification loss8 Minutes
- 12.3Classification Margins and Edge15 Minutes
- 12.4Classification Loss, Margins, Predictions and Edge for cross validated models11 Minutes
- 12.5Comparing two classifiers with holdout13 Minutes
- 12.6Computing Confusion Matrix8 Minutes
- 12.7Generating ROC Curve10 Minutes
- 12.8Generating ROC Curve based on the testing data9 Minutes
- 12.9More Customization and information while generating ROC6 Minutes
- 12.10Computing Accuracy, Error Rate, Specificity and Sensitivity (10)6 Minutes
I am Dr. Nouman Azam and I am Assistant Professor at the Department of Computer Science, National University of Computer and Emerging Sciences. I had over a decade of experience in research and teaching.
My research work is mainly on the exploration of machine learning techniques in application areas such as bioinformatics, text summarization, text categorization, email filtering, security, recommender systems and medical decision making.
In the recent past, I taught many machine learning related courses at the undergraduate and graduate levels.
MATLAB remained my number one choice for implementing ideas and converting my code to meaningful software’s. I implemented all the code of my Masters and Doctoral thesis in MATLAB.
Courses at Edugate:
1. Advance MATLAB Data Types and Data Structures
2. Complete MATLAB Tutorial: Go from Beginner to Pro
3. Create Apps in MATLAB with App Designer
4. Machine Learning Classification Algorithms Using MATLABS
5. MATLAB App Designing the Ultimate Guide for MATLAB APPS
6. LaTeX A-Z: From Beginner to Advanced in Less than 3 hours
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